System Parameter Identification
Information Criteria and Algorithms
Edited by- Badong Chen, University of Florida, Gainesville, USA
- Yu Zhu, Tsinghua University, Beijing, China
- Jinchun Hu, Tsinghua University, Beijing, China
- Jose Principe, University of Florida, Gainesville, FL, USA
Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors research provides a base for the book, but it incorporates the results from the latest international research publications.
Audience
Engineers, scientists and graduate students interested in information theory, signal processing, system identification and adaptive system training.
Hardbound, 300 Pages
Published: August 2013
Imprint: Elsevier
ISBN: 978-0-12-404574-3
Contents
Chapter 1
Introduction: system identification and criteria
Chapter 2
Main Information theoretic measures and Their propertiesChapter 3
Information theoretic parameter estimationChapter 4
System parameter identification: minimum error entropy criterionChapter 5
System parameter identification: minimum information divergence criterionChapter 6
System parameter identification: mutual information criterion
